Find Academic Supervisors

Bruce KapronView profile

Professor

• Logic in Computer Science
• Cryptography
• Foundations of Security
• +3 more

Bruce Kapron is a Professor of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. He holds a PhD from the University of Toronto (1991) and an M.Sc. in Mathematics from Simon Fraser University (1986). His research focuses on theoretical computer science, including computability, cryptography, computational complexity, and verification. He has held visiting positions at institutions such as Stanford University and the Institute for Advanced Study. Notable contributions include foundational work in higher-order computability, cryptographic protocols, and formal verification. Education: PhD in Computer Science from the University of Toronto (1991), supervised by Stephen Cook; M.Sc. in Mathematics from Simon Fraser University (1986), supervised by S. K. Thomason. Research interests emphasize logic in computer science, computational complexity, cryptographic foundations, and verification techniques. His work bridges theoretical insights with practical applications in security and algorithm design. Recent publications explore equilibrium complexity in games, separation logic, and pseudorandomness. He has advised numerous graduate students and holds major grants, including NSERC Discovery Grants focused on complexity theory and security foundations. His contributions extend to textbook authorship, such as *Logic, Automata, and Computational Complexity: The Works of Stephen A. Cook* (2023). Key professional milestones include roles as Distinguished Professor at Fondation Sciences Mathématiques de Paris (2022) and Visiting Fellow at the University of Bologna (2022). He actively participates in research networks and has delivered invited talks on topics such as NP-completeness and higher-order complexity theory.

Sema GunturkunView profile

Lecturer

• Commutative Algebra
• Monomial Ideals
• Gorenstein Rings
• +5 more

Sema Gunturkun is a Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. She holds a PhD in Mathematics from the University of Kentucky (2014), supervised by Uwe Nagel, and has held postdoctoral and visiting positions at the University of Michigan, University of Connecticut, and Amherst College. Her research focuses on Commutative Algebra, with specialties in monomial ideals, Gorenstein rings, Hilbert functions, syzygies, free resolutions, Linkage Theory, and intersections with Representation Theory. Before joining Essex, she served as a Visiting Assistant Professor at Amherst College (2019–2022) and University of Connecticut (2018–2019), and as a Postdoctoral Assistant Professor at the University of Michigan (2014–2018). Her work bridges abstract algebraic structures with applications in representation theory and algebraic geometry. Her publications span topics like Boij-Söderberg Theory, Eisenbud-Green-Harris conjectures, and neural ring polarizations. While no specific grants or awards are listed, her research demonstrates contributions to foundational areas of commutative algebra and interdisciplinary applications.

Dmitry SavostyanovView profile

Lecturer

• Numerical Mathematics
• Tensor Decompositions
• High-Dimensional Problems
• +3 more

Dr. Dmitry Savostyanov is a Lecturer at the University of Essex, School of Mathematics, Statistics and Actuarial Science (SMSAS). His research focuses on developing efficient algorithms for high-dimensional problems using low-rank tensor product approximations, linear algebra, and numerical methods. He holds a PhD in Computational Mathematics from the Russian Academy of Sciences and a PGCert in Higher Education from the University of Brighton. Qualifications: PhD (2006), MSc (2003), BSc (2001), PGCert (2016). Appointments: Senior Lecturer at University of Brighton (2014–2020), Senior Research Fellow at University of Southampton (2012–2014), and Visiting Research Fellow at University of Chester (2011–2012). His research interests span numerical mathematics, tensor decompositions, and computational methods for high-dimensional systems. Recent work includes applications in epidemiological modeling, quantum control, and NMR simulation. He has collaborated on software tools like the TT-toolbox for tensor train formats. Publications highlight contributions to tensor interpolation, quantum algorithms, and efficient numerical solvers. No scientific awards are explicitly listed, but his work reflects sustained innovation in computational mathematics. No student advisees or lab affiliations are detailed in the provided materials. Grants and funding sections exist but lack specific details.

Vasileios MaroulasView profile

Professor

• Computational Bayesian Statistics
• Topological Data Analysis
• Machine Learning
• +7 more

Vasileios Maroulas is a Professor of Mathematics at the University of Tennessee, Knoxville, with joint appointments at the Haslam College of Business and the Bredesen Center for Interdisciplinary Research and Graduate Education. He serves as Associate Vice Chancellor and Director of the AI Tennessee Initiative. His research focuses on computational Bayesian statistics, topological data analysis, machine learning, and their applications in science and engineering. He holds senior research fellow positions at the US Army Research Lab and is an Elected Member of the International Statistical Institute. Education: Ph.D. in Mathematics from the University of North Carolina at Chapel Hill. Research interests span computational probability, statistical learning, and interdisciplinary applications in materials science, medicine, and national defense. His work integrates topology, geometry, and machine learning to address complex data challenges. Key areas include Bayesian inference for persistent homology, quantum computing for topological analysis, and health analytics for opioid vulnerability modeling. His research is funded by agencies such as AFOSR, ARL, ARO, DOE, NSF, and the Simons Foundation. Notable achievements include the US Army Research Lab Fellowship, Leverhulme Visiting Fellowship, and UT’s Excellence in Research Award. He advises a dynamic research group (MRG) focusing on AI, Bayesian methods, and topological data science. Alumni include faculty members at the University of Hawaii and industry leaders in machine learning and data science. Current projects include quantum distance approximation, Bayesian sheaf neural networks, and spatiotemporal opioid vulnerability analysis. Labs/Teams: Maroulas Research Group (MRG) at the University of Tennessee, collaborating with national labs (ARL, ORNL) and industry partners (e.g., Eastman, Thor Industries).

Anindya Bijoy DasView profile

Assistant Professor

• Federated Learning
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Anindya Bijoy Das is a tenure-track Assistant Professor in the Electrical and Computer Engineering department at The University of Akron's College of Engineering and Polymer Science, where he teaches courses including Wireless Communications (Spring 2025) and Digital Communication (Fall 2024). Prior to joining Akron in August 2024, he served as a Postdoctoral Researcher at Purdue University (2022-2024) following completion of his Ph.D. at Iowa State University in 2022, where he received the prestigious Karas Award for outstanding dissertation work. His educational background includes: Ph.D. in Electrical Engineering, Iowa State University (2022) M.Eng. in Electrical Engineering, Iowa State University (2018) B.Sc. in Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (2014) Dr. Das's research focuses on cutting-edge areas at the intersection of machine learning, distributed systems, and communications. His primary interests include federated learning , AI/ML applications , distributed computation , information theory , and biomedical signal processing . Recent work explores the integration of large language models with traditional signal processing techniques, particularly for healthcare applications. His research bridges theoretical foundations with practical implementations, often addressing challenges in edge computing environments where computational resources are limited. The work demonstrates strong connections between theoretical information theory and practical system design. Analysis of his publication portfolio reveals an evolving research trajectory with increasing emphasis on federated learning architectures, privacy-preserving techniques, and the application of reinforcement learning to communication optimization. His work spans wireless communications, information theory, and healthcare applications, with a consistent focus on solving computational bottlenecks in distributed environments. The interdisciplinary nature of his research is evident in publications spanning IEEE Transactions on Information Theory, IEEE Journal on Selected Areas in Communications, and IEEE Signal Processing Magazine. His notable achievements include: Karas Award for Outstanding Dissertation in Mathematical and Physical Sciences and Engineering (2022) Research Excellence Award from Iowa State University (2021) Teaching Excellence Award from Iowa State University (2020) National Champion in Bangladesh Mathematical Olympiad (2008) Multiple Best Paper Awards at international conferences Dr. Das currently leads a research group focused on three main thrusts: improving federated learning algorithms, enhancing distributed computation schemes, and developing novel AI/ML applications. He has secured a $73,000 grant from Autonomous and Connected Systems of Purdue Engineering Initiatives for research on AI tensor computations in edge networks. Actively seeking 1-2 highly motivated PhD students, he emphasizes practical implementation alongside theoretical advances, with applications spanning healthcare, wireless communications, and edge computing environments. His service as a reviewer for top-tier journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Wireless Communications further demonstrates his standing in the research community.

Elizabeth GrossView profile

Professor

• Algebraic Statistics
• Computational Algebraic Geometry
• Phylogenetics
• +3 more

Elizabeth Gross is a Professor in the Department of Mathematics at the University of Hawaiʻi at Mānoa, joining in 2018. She holds a Ph.D. from the University of Illinois at Chicago (2013). Her research focuses on algebraic statistics, computational algebraic geometry, and their applications in phylogenetics and systems biology. She has led interdisciplinary projects such as the NSF-funded CAREER Grant (2020) to advance phylogenetic network analysis using algebraic methods. Her academic contributions span theoretical advancements in algebraic statistics, including work on model identifiability and steady-state analysis of chemical networks. She has also explored neural coding and information literacy in educational contexts, emphasizing collaboration between librarians and researchers in AI literacy initiatives. Key achievements include developing methodologies for phylogenetic network reconstruction, analyzing convex/non-convex neural codes, and advancing computational tools like Bertini for Macaulay2. Her work bridges pure mathematics with applied domains such as biology and social network analysis. Professional recognitions include the 2020 NSF CAREER Award. She actively participates in academic service, contributing to conference organization (e.g., Algebraic Statistics 2020) and maintaining a research group focused on algebraic approaches to interdisciplinary problems.

Yan ZhangView profile

Assistant Professor

• Combinatorics
• Blockchain
• Supersymmetric Representation Theory
• +4 more

Yan Zhang is an Assistant Professor of Mathematics and Director of CAMCOS at San Jose State University (SJSU), within the College of Science's Department of Mathematics and Statistics. He holds a BA from Harvard University (2003–2007) and a PhD in Applied Mathematics from MIT (2008–2013), advised by Richard Stanley. Prior to SJSU, he was a Morrey Visiting Assistant Professor at UC Berkeley (2013–2016). His research focuses on algebraic combinatorics, blockchain technology, supersymmetric representation theory (via Adinkras), and interdisciplinary applications. Notable projects include collaborations with the Ethereum Foundation on blockchain theory and the design of SJSU’s Cryptography course. He also directs SPARC, a summer program for gifted high school students. Zhang’s work bridges discrete mathematics with physics, biology, and social sciences. He has contributed to topics like judgment aggregation, neural coding, and chip-firing games. His teaching and mentoring span university and non-traditional settings, emphasizing problem-solving and interdisciplinary thinking. Scientific awards include the Best Student Paper Award at the 2012 FPSAC conference. His collaborations extend to industry, including consulting for startups like Cerebras and the Ethereum Foundation. Zhang’s academic contributions are further reflected in his extensive publication record and invited talks at institutions such as MIT, UC Berkeley, and Stanford.

James KimballView profile

Teaching Professor

• Differential Geometry
• Algebraic Geometry
• Coding Theory

James Kimball serves as a Teaching Professor and Assistant Department Head at the University of Louisiana at Lafayette. He holds roles such as Master Instructor and Director of Freshman Math, supporting STEM education through course development and instructor coordination. His academic journey includes a Ph.D. (2008) and M.S. (2004) from Texas A&M University, and a B.S. (2000) from Louisiana College. His research focuses on differential geometry, algebraic geometry, and coding theory, reflecting a blend of theoretical and applied mathematical interests. As an educator, he prioritizes innovative teaching methods, particularly in online course creation and freshman-level mathematics instruction.

Robin PemantleView profile

Professor

• Probability Theory
• Analytic Combinatorics
• Stochastic Processes
• +4 more

Robin Pemantle is a Professor of Mathematics and affiliated with the Computer and Information Science department at the University of Pennsylvania's School of Engineering and Applied Science. He holds primary appointment in the Department of Mathematics. His research spans probability theory, analytic combinatorics, stochastic processes, and mathematics education. He co-authored influential works such as There is No One Way to Teach Math (2024) and Analytic Combinatorics in Several Variables (2024), emphasizing active learning strategies and combinatorial methodologies. Research interests include asymptotic analysis of generating functions, percolation theory, and applications of probability to biology and networks. He has advised over 20 PhD and Master's students, focusing on topics like random walks, combinatorial models, and education technology. Pemantle leads initiatives like the Penn Calculus Project, redesigning calculus curricula with active learning principles. His work bridges pure and applied mathematics, with contributions to statistical physics (e.g., Ising models) and theoretical computer science. Notable recent studies address invasion percolation on trees, trace reconstruction algorithms, and aggregation methods for probabilistic forecasts.

Harald NiederreiterView profile

Researcher

• Numerical Analysis
• Pseudorandom Numbers
• Quasi-Monte Carlo Methods
• +6 more

Harald Niederreiter is a Senior Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), part of the Austrian Academy of Sciences. He has held prominent roles including Director of the Institute of Discrete Mathematics and Institute of Information Processing (Austrian Academy of Sciences), and Professorships at the University of Illinois at Urbana-Champaign and National University of Singapore. His research spans numerical analysis, cryptology, finite fields, and coding theory. Former positions: Directorships at multiple institutes, Professorships at UIUC and NUS Visiting scholarships at institutions including IAS Princeton, UC San Diego, and ETH Zurich Research interests focus on computational methods for PDEs, pseudorandom number generation, quasi-Monte Carlo techniques, and applied algebra. He is a Fellow of the American Mathematical Society and has received the Singapore National Science Award and the Cardinal Innitzer Prize. No specific grants or advising records are listed in the provided text. His work is affiliated with RICAM's research groups in mathematical data science and optimization.

Andreas RingwaldView profile

Visiting Professor

• Particle Physics
• Theoretical Physics
• Astroparticle Physics
• +3 more

Andreas Ringwald is a particle physicist at DESY and Visiting Professor at Durham University's Institute of Particle Physics and Phenomenology. As Head of the ALPS Group and co-spokesperson of the ALPS Collaboration, he leads research on non-collider particle physics including light neutral bosons, millicharged particles, and fundamental physics with X-ray lasers. His research develops novel approaches to detect weakly interacting particles through experiments like light-shining-through-walls techniques. Major theoretical contributions include the discovery of T regulatory type 1 cells and advancing quantum field theory applications in cosmology. Recent publications focus on superconducting magnet technology for next-generation particle accelerators, including optimization of quench protection systems, Nb₃Sn dipole development, and diagnostic methods for superconducting circuits.

• Computer Science
• Numerical Analysis
• Algebraic Algorithms
• +4 more

Dr. Victor Y. Pan is a Distinguished Professor of Mathematics and Computer Science at Lehman College, The City University of New York (CUNY). He has been affiliated with Lehman College since 1988 and holds one of the highest academic ranks reserved for influential scholars. His research focuses on numerical and algebraic algorithms, with a particular emphasis on polynomial computations, matrix structures, and root-finding methods. Dr. Pan's work bridges numerical and symbolic computing, aiming to optimize computational efficiency while ensuring accuracy. Educational Background: Ph.D. in Mathematics from Moscow University Research experience at the Soviet Academy of Science Research Interests: His key areas include polynomial root-finding, matrix eigenproblems, structured matrices (e.g., Toeplitz, Hankel, and Cauchy), and low-rank approximation. He has pioneered methods combining numerical and algebraic techniques to enhance computational speed and precision. Recent work emphasizes algorithms for sparse polynomials, superfast root-finders, and efficient matrix computations. Publications & Impact: With over 200 peer-reviewed papers and three books, Dr. Pan’s research has influenced global computational mathematics. His articles address topics like fast root-finding, matrix eigenvalue problems, and low-rank approximation at sub-linear cost. His work is widely cited in computer science and applied mathematics. Awards & Recognition: Appointment as Distinguished Professor (CUNY, 2000) Global recognition as a leader in theoretical computer science and numerical analysis Advising & Grants: Recipient of continuous NSF funding for over 20 years. He has mentored 17 Ph.D. students through his seminar program, focusing on algebraic and numerical computing. His seminar fosters collaborative research in topics like polynomial equations, coding theory, and eigen-solving techniques. Labs & Teams: Leads the Algebraic Numerical Computing Seminar at CUNY’s Graduate Center, integrating Computer Science and Mathematics students. The seminar explores cutting-edge topics such as displacement-structured matrices, polynomial root-finding, and eigen-solving algorithms.

Nathan CarterView profile

Professor

• Mathematics Education
• Data Science
• Computer Graphics
• +2 more

Nathan Carter is a Professor of Mathematical Sciences at Bentley University, blending computer science with mathematical education through innovative software tools and award-winning textbooks. His research focuses on visualization in abstract algebra, data science pedagogy, and educational technology development. Awards: Beckenbach Book Prize (2012, 2016) MAA Henry L. Alder Award for Distinguished Teaching Publications: Authored foundational texts including Visual Group Theory (2009), Mathematics of Computer Graphics (2016), and edited Data Science for Mathematicians (2020). These works facilitate the teaching of complex mathematical concepts through visual and computational approaches. Software Development: Creates open-source educational tools for mathematical logic and abstract algebra visualization, such as the proof-checking software Lurch. His work emphasizes bridging theoretical mathematics with practical computational skills.

Percival MatthewsView profile

Associate Professor

• Math Education
• Mathematical Cognition
• Fraction Understanding
• +3 more

Percival Matthews is an Associate Professor in the Department of Educational Psychology at the University of Wisconsin-Madison and serves as Associate Dean in the School of Education. His research focuses on mathematical cognition, particularly how people learn about fractions, ratios, and algebraic concepts, aiming to translate findings into effective pedagogical strategies. He holds a PhD in Psychology from Vanderbilt University (2010), an MA in Political Theory from the University of Chicago (2001), and a BA in Physics from Harvard University (1997). Key research areas include the nonsymbolic ratio processing system, the development of fraction understanding, and the role of the equals sign in algebraic readiness. His work combines behavioral experiments, neuroimaging (e.g., fMRI studies of fraction processing), and educational interventions. Matthews leads the Math Education Learning & Development (MELD) Lab, which investigates cognitive foundations of mathematical competence and their application to K-12 education. His awards include the Impact 2030 Faculty Fellowship (2020), NIH Loan Repayment Award (2017, 2020), and McDonnell Foundation's Understanding Human Cognition Scholar award (2018). He has secured grants from the NSF and NIH to study fraction cognition, including projects on nonsymbolic ratio perception and neural correlates of mathematical learning. Matthews collaborates with institutions like the EdNeuroLab and has mentored numerous graduate students and postdoctoral researchers in educational psychology and cognitive science.

Kehelwala Dewage Gayan MadurangaView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +4 more

Kehelwala Dewage Gayan Maduranga is an Assistant Professor in the Department of Mathematics at Tennessee Tech University, where he joined in 2022. His educational background includes: B.S. in Mathematics from University of Sri Jayewardenepura (Sri Lanka, 2009) M.S. in Mathematics from Tennessee Tech University (2013) Ph.D. in Mathematics from University of Kentucky (2020) Dr. Maduranga's research focuses on machine learning and deep learning applications, with particular expertise in Recurrent Neural Networks (RNN) and other network architectures. His work spans hyperparameter optimization for computer vision applications and the exploration of unitary and symmetric structures in deep neural networks. His research bridges theoretical mathematics with practical AI applications, leveraging his strong foundation in both pure mathematics and computational techniques. His publication record shows a clear progression from theoretical mathematics (2016) to increasingly applied machine learning research (2019, 2022), demonstrating a trajectory that connects abstract mathematical concepts with cutting-edge AI techniques. His work on symmetry-structured convolutional networks and complex unitary recurrent networks represents innovative approaches to improving neural network architectures through mathematical insights. While specific awards are not mentioned in the available information, his research has been published in reputable venues including Springer's Neural Computing and Applications and the AAAI Conference on Artificial Intelligence. Dr. Maduranga maintains an active research presence, with code repositories available on GitHub that implement his published research, including implementations of symmetry-structured convolutional neural networks and complex unitary RNNs. His work appears to focus on developing mathematically principled approaches to improve the efficiency and effectiveness of neural network architectures.